The Impact of internet user shopping patterns and demographics on

Journal of Electronic Commerce Research, VOL. 6, NO.3, 2005

THE IMPACT OF INTERNET USER SHOPPING PATTERNS AND DEMOGRAPHICS ON CONSUMER MOBILE BUYING BEHAVIOUR Enrique Bigne Department of Marketing University of Valencia [email protected] Carla Ruiz Department of Marketing University of Valencia [email protected] Silvia Sanz Department of Marketing University of Valencia [email protected]

ABSTRACT In less than 15 years, the mobile phone has become an essential part of our daily lives. It is no longer a luxury item and has become one of the most commonly used daily consumer goods throughout the world. Despite its growing significance, there is still a lack of research work in many countries in this field. This study aims to make an approximation of the M-shopper profile and identify the variables influencing purchase behaviour. The methodology used to achieve these objectives is based on 7 focus groups and the analytical techniques of ChiSquare and logistic regression. Analysis of the results obtained from a representative sample of 2,104 Spanish Internet users shows that the M-commerce decision can be predicted according to consumer age, social class and behaviour patterns as user of non-store shopping channels. Keywords: Consumer behaviour; Direct shopping; Home Shopper; M-Commerce 1. Introduction Technological progress in the sphere of information and communication is encouraging the use and development of new shopping methods, leading to a rapid growth in non-store shopping as the individual can buy products/services without having to travel to retail outlets [Sharma and Sheth 2004; Thompson 1997]. This growth in non-store shopping and new trends in technology have facilitated the introduction of electronic marketing and promise to provide new ways of impacting and serving consumers in the future [Balasubramanian, Peterson and Jarvenpaa 2002; Reynolds 2000; Sivanad, Gesta and Sulep 2004]. Practically all products/services can be purchased quickly, conveniently and without moving from home [Davison, Dorrington and McCoy 1982; Eroglu, Machleit and Davies 2003; Rosenberg and Hirschman 1980; Sheth 1983]. Wireless Internet via mobile devices (WIMD) is leading the world into another spectrum of communications and means of conducting day-to-day business and life activities [Sivanad, 2004]. In the next years, more Internet transactions will be realized via mobile phones than fixed network devices. According to the MC Statistics viewed at epaynews.com, only 16% of 533 million Internet users were global wireless Internet users in the year 2001; however the percentage will soar to 57% of 1460 million Internet users in 2007. At present, online ticketing, reservation for flights and hotels or theater tickets are part of the most attractive WAP applications, since they bring consumers comfort, spontaneity and mobility [Buellingen and Woerter 2004]. In general, the different methods of direct shopping are still expected to grow but whereas some of the methods are showing rather low levels of growth, stagnation and even decline, in others the predicted growth rate is picking up speed. The most innovative methods maintain significant rates of growth [Sivanad 2004; Yang 2005] and at the same time there is a tendency to use them in conjunction with other sales systems as companies seek to complement sales methods and obtain synergies [Wu and Wang 2004]. Thus for example, the combination of Internet with other methods, such as the mobile phone or television will make it possible to optimise consumer convenience and

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Bigne et al.: The Impact of Internet User Shopping Patterns and Demographics

increase their scope wider audience, in addition to overcoming some of the limitations which arise when using Internet alone as a shopping tool [Buellingen and Woerter 2004; Chiles and McMackin 1996; Jarvenpaa et al. 1999; Lohse and Spiller 1998; Swaminathan et al. 1999]. In Spain, the field study “Surfers on the net” [AIMC 2005] highlights the convergence of Internet with other media, and reveals that 45.4% of Internet users have sometimes watched television through Internet, 76.7% have sometimes used Internet to listen to the radio and 92.2% have sometimes consulted an e-newspaper. It should be emphasised that the public at home use not only Internet but also the new mobile technologies such as electronic diaries and mobile phones. According to the study “Surfers on the net” [AIMC 2005], 37.8% of Internet users have a laptop computer, almost all of them (95.0%) have a mobile phone, 45.6% have a Web Cam and 16.0% have PDAs. These results suggest that the Internet user has a positive attitude to new technologies. With the explosive growth of the mobile phone population combined with the development of wireless technologies, M-Commerce is becoming increasingly important to many businesses nowadays [Hung et al. 2003]. According to Wireless Week [2004] there were 94.9 million M-Commerce users worldwide in 2003 and the segment is expected to grow to 1.67 billion by 2008 [Yang 2005]. Table 1 shows mobile phone penetration in Europe during the period 1997-2002, measured as the percentage of number of lines in relation to the population in each of the member states. In comparison to other EU countries, mobile phone penetration in Spain is above the European average and higher than that of the two main economies in the Economic and Monetary Union (Germany and France) [TMC 2003]. In the Spanish case, the climate favours the use of laptops to the detriment of desk tops. Table 1. Mobile Phone Penetration in Spain and the European Union Year Spain Germany France Ireland Italy 1997 10.8 10.1 10.0 14.6 20.4 1998 16.3 17.0 19.2 25.6 35.6 1999 38.1 28.6 36.6 44.3 52.6 2000 59.9 58.7 49.5 65.2 73.2 2001 72.1 68.4 60.8 77.6 88.6 2002 80.1 71.8 65.0 76.4 90.2 Source: TMC [2003].

Portugal 15.2 30.9 46.8 65.4 77.7 82.5

UK 15.0 25.2 45.8 72.9 77.3 82.0

E.U. 14.1 24.0 40.8 63.3 76.4 79.3

At present, the Spanish mobile market has a penetration rate of 86% with 36 million mobile subscribers [Netsize 2004]. With an increase of 374 thousand mobile users in April 2003, Spain saw the second largest surge in its customer base of any European country, behind Greece and ahead of Denmark [Netsize 2004]. Spain has three mobile operators (see Table 2). With more than 18 million subscribers and a 53.5% market share, Telefónica MoviStar dominates the Spanish market. Over the last two years, Amena has seen its market share grow considerably to reach nearly 20% of the Spanish mobile market in 2003. Around 60% of Spanish end-users are prepaid users. Table 2. Spanish mobile telecom operators Telefónica MoviStar (www.tme.es) Main shareholders Telefónica (94.44%) End-user market share in millions 2001 15.71 (55.8%) 2002 18.10 (56.1%) 2003 18.87 (53.5%) Total turnover in millions 2002 6,817.0 2003 3,469.6 Monthly ARPU (€) 2002 28.8 2003 28.6 Source: Netsize [2004].

Vodafone (www.vodafone.es) Vodafone Group (91.6%)

Amena (www.amena.es) Auna (97.9%)

7.55 (29%) 8.15 (25.3%) 9.18 (26%)

4.87 (17.3%) 6.00 (18.6%) 7.22 (20.4%)

3,413.23 2,047.19

2,229.81 n/a

31.0 31.0

28.2 n/a

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Customization services represent 50% of the total mobile data usage (see table 3). Vote services account for a significant part of data traffic, thanks to the popularity of TV shows like El Gran Hermano, Operación Triunfo and La Isla de los Famosos. Table 3. Mobile data services in Spain % of total data services Logos and images 50% Ringtones 50% Adult content 5% Games 5% Quiz 5% Chat 5% Adult chat 10% Vote 20% Information service 5% Source: Netsize [2004]. The increased mobile usage of recent years is a clear example of the system’s growth, significance and the opportunities it offers as an independent sales channel and it therefore merits special attention from researchers. While published work on M-commerce applications and technologies and the different mobile operators and their services is becoming more abundant and representative [Barnes 2002, Buellingen and Woerter 2004; Coursaris and Hassanein 2002; Dholakia and Dholakia 2004; Figge 2004; Gerstheimer and Lupp 2004; Lehrer 2004; Leung and Antypas 2001; Kumar and Zahn 2003], there is a lack of literature on the profile of users who buy products/services through the different mobile operators and on the analysis of the factors which most influence shopping behaviour and the processes of adopting M-commerce [Coursaris and Hassanein 2002; Luarn and Lin 2004; NG-Kruelle et al. 2002; Wu and Wang 2004; Yang 2005]. Thus, the main objective of this work is to make an approximation (sociodemographic and behavioural) of the M-shopper profile based on a sample of Internet users and identify which variables exercise a significant influence on M-commerce. To achieve these objectives, this work is organised into two parts. The first part, theoretical in content, includes qualitative research, in-home shopping literature review and developing of the working hypotheses. The qualitative research is done to identify the values which are the primary drivers of consumers in mobile shopping as a complement to the theoretical framework and to develop some of our hypotheses. The second part includes the methodology used in the empirical study done on a representative sample of 2,104 Spanish Internet users and the data analysis. The M-shopper profile is described and the incidence of different variables on purchase behaviour is examined. In particular, the influence of non-store shopping experience and exposure to Internet on Mcommerce is analysed. We also suggest gender, age and social class influence on consumer Mobile shopping behaviour. 2. Literature review and hypotheses development Here we review the literature on non-store shopping and present a conceptual model for analysing the factors which influence M-shopping. In order to complete the literature review of primary driver values for consumers, we carried out a qualitative phase of research. Qualitative research is becoming increasingly used in marketing to complement quantitative methods [De Ruyter and Scholl 1998; Srinivasan, Anderson and Ponnavolu 2002; Szymanski and Hise 2000], and is appropriate to determine if consumer reactions to new products or processes as consumer ideas, opinions and feelings can be examined in-depth, behaviour patterns can be identified and shopping behaviour explained [Crabtree and Miller 1992; De Ruyter and Scholl 1998]. As an illustration of this, Szymanski and Hise [2000] identified the possible antecedents of customer satisfaction in virtual environments using the focus groups technique and Srinivasan, Anderson and Ponnavolu [2002] carried out in-depth interviews to identify the factors which influence e-customer loyalty. In this study, qualitative research helped us to develop some of the hypotheses of the conceptual model. We conducted 7 focus group interviews of homogeneous participants. All of them were Internet and Mobile phoneusers. On average, they spent one hour using Internet per day and they had used the Mobile for about three years. Some of these respondents use the Internet and Mobile for shopping. The size of the focus group was six to ten participants. The dialogue was captured on tape. This study was conducted from March to April 2004 (see Table 4).

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Table 4. Group Structure Group meeting

Group 1 (3 dynamics)



Gender

Both genders

Both genders

Job

Spanish university students

Both genders Senior and middle management 40-55 100% Users/ 10% Shoppers 100% Users/ 10% Shoppers

Age

18-25 100% Users/ Online Users/Shoppers 20% Shoppers 100% Users/ Mobile Users/Shoppers 40% Shoppers Date of meeting March to April 2004

Students from EU 18-25 100% Users/ 60% Shoppers 100% Users/ 20% Shoppers

The literature review on adoption levels suggests that consumer needs, interests and attitudes vary with age, and the youngest consumers have the most positive attitude toward innovation [Darian 1987; Modahl 2000; Mulhern 1997; Rogers 2003; Schiffman and Kanuk 2003; Steenkamp, ter Hofstede and Wedel 1999; Wotruba and Pribova 1995]. Given that the variable age influences attitude towards this new medium, participants in group 2 were over 40 years old, while those in the other groups were aged between 18 and 25. In the context of interactive shopping, cultural characteristics also influence shopping patterns [Park and Jun 2003; Van Birgelen, De Ruyter, De Jong and Wetzels 2002]. Thus, consumers from individualist cultures develop a more innovative profile and are more predisposed to shop via Internet than consumers from cultures which favour collectivism [Jarvenpaa, Tractinsky, Saarinen and Vitale 1999; Steenkamp, ter Hofstede and Wedel 1999]. For this reason, the participants in group 1 were Spanish university students (students from low-individualist cultures), while those in group 3 were Norwegian and German students, i.e. from highly individualist cultures. The conceptual model of M-commerce in Figure 1 is one outcome of this qualitative phase of research and literature review as explained below.

Gender Sociodemographics

H.1

Age H.2

Social Class H.3

ICT relationship “Technological Cluster”

Experience as online shopper Internet exposure

M-Commerce

H.5

H.4

Figure 1. Conceptual Model 2.1. Sociodemographics The literature review shows a great number of studies on the in-home shopper profile [Cunningham and Cunningham 1973; Darian 1987; Gillet 1970; Korgankoar and Moschis 1987; May and Greyser 1989; Mulhern 1997; Peterson et al. 1989; Reynolds 1974; Reynolds and Darden 1972; Wotruba and Pribova 1995], and the Internet shopper [Dholakia and Uusitalo 2002; Vrechopoulos et al. 2001; Sim and Koi 2002], but there are no specific studies on the M-shopper profile, possibly because this object of study is so new. If we focus on the demographic characteristics of the in-home shopper, in general, the higher the level of education, income and occupation of the head of the household, the more favourable the perception of non-store

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shopping [Cunningham and Cunningham 1973; Darian 1987; Gillett 1970; Mulhern 1997; Peterson et al. 1989; Reynolds 1974; Reynolds and Darden 1972; Wotruba and Pribova 1995]. One of the most successful systems of direct sales is Internet. In relation to the sociodemographic profile of the Internet shopper, the literature review reveals that system’s main users are young men, with a high level of income and a university education [Brennan 2000; Dholakia and Usitalo 2002; Hoffman et al. 1995; Joines, Scherer and Scheufele 2003; Li et al. 1999; Vrechopoulos, Siomkos and Doukidis 2001], however, this profile is changing and in the United States is becoming like that of the average consumer to encompass all genders, ages and social classes. For example, in USA in the early years of Internet there were very few women users, but by 2001 women were 52.8% of the online population [Siegel 2003]. Internet shoppers have a positive attitude towards Internet [Park and Jun 2003; Sim and Koi 2002], enjoy surfing the net [Park and Jun 2003, Sim and Koi 2002], are more innovative than non Internet shoppers [Donthu and García 1999], trust their ability to make a purchase without prior inspection of the products [Dholakia and Uusitalo 2002] and usually use other technologies [Bellman et al. 1999]. The study of how gender relates to the purchase decision has always been of interest in the academic world, since in many product categories, women make the purchase decision [Dholakia 1999; Hawfield and Lyons 1998]. If we focus on the non-store shopping arena, the influence of gender on the specific behaviour of the collective of shoppers is complex and the literature review reveals contradictory results. Several authors state that women are the main users of direct shopping media [Darian 1987; Peterson, Albaum and Ridgway 1989; Wotruba and Pribova 1995]. However, the freedom and independence provided by these methods mean they are also attractive to men who usually have mainly utilitarian purchase motivations, valuing personal contact and social relations to a lesser degree [Citrin et al. 2003; Cross and Madson 1997; Hofstede 1980; Steenkamp et al. 1999]. Sociocultural pressure has made men generally more independent in their purchase decisions, while women place greater value on personal contact and social relations [Cross and Madson 1997; Citrin et al. 2003]. Society has traditionally favoured affective relations among women (for example, the development of shared activities with adults of the same sex), which entails greater interdependency in their social relations. In contrast, men need to develop their own identity, even if it means repressing their affective and relational needs. These schemes of interdependency and separation do not only influence the individual’s personality, but also his/her attitude and behaviour towards different situations such as shopping [Markus and Oyserman 1989].Thus, men’s purchase motivations are usually mainly utilitarian [Hofstede 1980; Steenkamp et al. 1999] while social and personal motives (entertainment to break routine, the chance of socialising in a public place, interaction with other consumers, etc.) increase satisfaction with shopping and encourage women to continue frequenting traditional commercial establishments. Many women consumers see shopping as a source of stimulation and inspiration and place special value on the act of shopping itself [Sim and Koi 2002]. In general, consumers think that opportunities for interaction with sales personnel or the opportunity to socialise with other consumers are lower in virtual channels in comparison to traditional ones, but find greater efficiency (savings in time, money, etc…) in non-store shopping [Rohm and Swaminathan 2004; Trocchia and Janda 2000]. Given that men are mainly guided by utilitarian motivations and women by hedonistic ones, and male shoppers mind less the sacrifices they need to make in non-store shopping (i.e. the inability to touch a product before making a purchase or the lack of a social environment and enjoyable ambience), it is to be expected that men place greater value on the benefits of non-store shopping [Citrin, Stern, Spangenberg and Clark 2003; Marcus and Kitayama 1991]. Research by Yang [2005] also evidences that males tend to perceive M-Commerce favourably. With that in mind, we offer the following hypothesis: H1) Among Internet users, men are more predisposed to M-commerce adoption than women. As for the age variable, users of non-store shopping systems are young, as the new technological advances in some direct shopping methods require account to be taken of the individual’s capacity to understand the changes and complexities in the new technologies and to develop a positive attitude towards them [Mulhern 1997; Peterson et al. 1989; Rogers 2003; Wotruba and Pribova 1995]. For example, the presence of children in the household favours non-store shopping [Darian 1987; May and Greyser 1989; Modahl 2000]; however, the elderly segment of the population is very reticent to adopt the new sales channels, since they have no time restrictions and enjoy traditional shopping [May and Greyser 1989]. Trocchia and Janda [2000] maintain that the lack of experience with the new technologies, resistance to change and the need to have a visual and/or tactile experience of the product before the purchase, are the main reasons why mature consumers do not shop by Internet. Lack of familiarity with the medium prevents mature consumers from evaluating the benefits Internet offers and means that they tend to prefer to use other information sources for their purchases such as the television, press or social interaction with friends and relations. Young people are more favourably disposed towards change [Modahl 2000; Mulhern 1997; Schiffman and Kanuk 2003] and use the new technologies such as the mobile phone from a very early age [AIMC 2004]. They also

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have hedonistic and utilitarian motives for using Internet and mobiles, considering them a source of information, communication, entertainment and an alternative shopping channel [Bordeau et al. 2002]. The attitude towards innovation and these differentiated needs are one of the main reasons for the growth in personal computers and mobiles in households [Modahl 2000], since young people are motivated to use these media for something more than just education or work [Bordeau et al. 2002; Joines, Scherer and Scheufele 2003]. As our focus members indicated: “I think that despite the low level of direct shopping by television, mobile and Internet in Spain, they are the shopping channels of the future and therefore will become more generally used, displacing traditional shopping channels but without totally replacing them” (18-25 years old). “I consider that direct shopping will evolve slowly in Spain and that it will not be a threat to traditional sales channels. However, my intention is to purchase through the mobile, to a greater or lesser extent, when I am offered a product or service which meets my specific needs” (18-25 years old). “I think that the new technologies are only necessary in critical situations (e.g. in the case of a car break down, etc…) or to improve the quality of life for people who are physically handicapped, but I don't consider their use in all aspects of daily life” (40-45 years old). “Although sometimes I find it difficult to use the mobile, my son helps me to solve any problems which come up. For example, he downloaded my favourite tune. I think that generational change will be a driver to interactive shopping due to their constant exposure to and familiarity with the new technologies” (40-45 years old). Hence, we hypothesise that: H2) Young people are more predisposed to M-commerce adoption than older Internet users. The middle classes appear to be the most receptive segment to direct sales [AIMC 2005]. However, the exclusive nature of certain products in these channels and existing consumption habits mean that it is beginning to gain special acceptance in the highest segments of the population [Liberal, 1997]. In addition, the view of other authors [Rosenberg and Hirschman 1980] should also be kept in mind. For them, the new sales systems are made for everyone, and attempt to reduce the distinctions between social classes by offering a wide variety of products and services for all consumers. In the case of the mobile phone, most of the products offered such as logos, ringtones, games, voting programs, etc, are especially attractive to the middle class segment of the population because they boost social integration. These sentiments are echoed in the following statements from our focus group members: “I would never participate in voting TV programs because I don’t think that the content of this type of program is appropriate to my lifestyle” (high-class senior manager). “I enjoy participating in voting TV programs by sending SMS messages. I enjoy watching celebrities and sending them encouragement or criticising them with my friends” (medium-class student). We therefore suggest that: H3) Internet users from the middle classes are more predisposed to use the mobile as a shopping channel. 2.2. ICT Relationship It should remembered that an influential factor in consumer attitude towards non-store shopping is exposure to technology, since it has been demonstrated that increased exposure to technology increases the probability of developing favourable attitudes towards new shopping channels [Modahl 2000]. Several authors maintain that consumers with most exposure to new technologies are those who are more willing to adopt direct channels [Bellman et al. 1999; Dholakia and Uusitalo 2002; Korgankoar and Moschis 1987; Lohse, Bellman and Johnson 2000; Modahl 2000; Sim and Koi 2002]. The increase in use and knowledge of these media will doubtless modify the norms of behaviour and response given to marketing actions [Dahlen 2002]. According to literature review, we expect Internet exposure to lead to M-shopping. The following quotes are illustrative: “I am happy that I have got a high performance computer both at work and at home. I can play games, download music and especially, listen to ringtones which I end up buying through the mobile” (on line user). “When the battery runs out on my mobile I send text messages by Internet” (on line user). “My awareness of the mobile’s benefits as a shopping channel has grown over a very short time, mainly due to the communication campaigns in media such as the Television or Internet (on-line user)”. “I don’t understand why you don’t use Internet or the mobile more in Spain to notify class changes, provide study material, to pay the registration fees, etc., like they do in Norway. Perhaps it’s because here most students don’t even have a laptop or because connection costs are much higher. I think that both Internet and the mobile are good information and shopping channels and their use should be encouraged at University” (individualistic culture student: Norwegian). Hence we hypothesise that: H4) Exposure to Internet has a positive influence on the use of the mobile as a shopping channel.

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Rogers [2003] argues that “the adoption of one new idea may trigger the adoption of several others in a cluster which consists of one or more distinguishable elements of technology that are perceived as being interrelated”. The technology cluster concept has been used to examine the adoption of videotext [LaRose and Atkin 1992], ECommerce [Eastin 2002] and Mobile-Commerce [Yang, 2005]. This concept posits that consumers are likely to adopt a technology offering the same functions as those already adopted. Mobile commerce is a technology developed from computers and communication technologies so consumers who adopt cell phones, PDA, notebook computer or on-line shopping, should be more likely to adopt M-Commerce. Finally, it should be highlighted that some prior experience with non-store shopping channels has a positive influence on the adoption of new direct shopping channels, since the consumer acquires skill in purchasing products/services with no prior physical inspection [Dholakia and Uusitalo 2002; Korgankoar and Moschis 1987; Shim and Drake 1990]. Hirschman [1980] noted that understanding of the characteristics and operation of a new product (sales channel) is needed before it can be adopted. In the context of M-commerce, it is to be expected that consumers with more experience of non-store shopping should also use M-shopping. Regarding non-store experience, the focus group members indicated: “It is even easier to purchase through the mobile than on Internet, that’s why I now use both shopping channels” (on line shopper). “I used to buy tunes on Internet but now I buy them on the mobile” (on line shopper). “I always prefer to buy in a traditional store because I can see and touch the products and the seller can solve any problems I might have” (non on line shopper) “I would buy any type of product on Internet provided it cost less than through traditional channels. For example, I bought this sweater through Internet. I think the mobile is also a good tool for shopping, I’m not bothered which of the two shopping channels I use” (online individualistic student: German). Hence, we propose that: H5) Experience as an Internet shopper has a positive influence on the M-commerce adoption. The following table is a summary of the literature review and the main contributions of the qualitative analysis, which support the hypotheses proposed in this study (see Table 5). 3. Methodology In this study we examined secondary data from a survey by the Spanish Association of Electronic Commerce [AECE 2004]. The study of Electronic Commerce B2C collected information using Computer Assisted Telephone Interview (CATI) method from March to April 2004. Complete responses to the measures were obtained from a random sample of 2,104 survey participants. The population of this study was Spanish Internet users over 14 years old. The logistic regression technique was used to test the proposed hypotheses. With this technique, data is treated with a dependent, non-metric variable, offering several advantages in comparison to discriminant analysis when the dependent variable has only two categories as in the case of the dependent variable M-commerce decision [Hair et al., 1998]. It is more robust than discriminant analysis when the assumptions of multivariate normality and equality of the variance-covariance matrixes are not fulfilled between the groups, it facilitates treatment with independent variables and the results obtained are parallel to those of multiple regression in terms of interpretation and the case by case diagnostic measurements available for residue examination. All the variables in the analysis were measured directly. The non-store shopper’s experience was measured with a dichotomous variable in which individuals indicated if they had acquired a product or service through Internet during the year 2003. In the case of exposure to Internet and following the same criteria used in other research work [Goldsmith, 2002] frequency of Internet access (from "everyday" to "at least once a month") was considered to be a representative item for this concept, since individuals who connect more often have more experience than those users who only connect occasionally. The dependent variable is M-shopping (purchase decision). A dichotomous scale was used to measure this with two possible categories of reply: those individuals who have acquired a product or service through the mobile and those who have never shopped through this medium. 4. Data Analysis Firstly, a descriptive analysis of the sample was done. Then, the Chi-Square technique allowed us to test the existence of significant differences between the socio-demographic and behavioural profile of M-shopper and non M-shopper. Secondly, a logistic regression analysis was done to test the influence of consumer profile and relationship with the Internet medium on the M-shopping decision.

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Table 5. Hypotheses development Hypotheses

H1: Among Internet users, men are more predisposed to M-commerce adoption than women.

H2: Young people are more predisposed to M-commerce adoption than other Internet users.

H3: Internet users from the middle classes are more predisposed to use the mobile as a shopping channel.

H4: Exposure to Internet has a positive influence on the use of the mobile as a shopping channel.

H5: Experience as an Internet shopper has a positive influence on the M-commerce adoption.

Qualitative Research Conclusions

Literature Review Bellman et al., 1999; Brennan, 2000; Citrin, et al., 2003; Cross and Madson, 1997; Cunningham and Cunningham, 1973; Darian, 1987; Dholakia, 1999; Dholakia and Uusitalo, 2002; Donthu and García, 1999; Gillet, 1970; Hawfield and Lyons, 1998; Hoffman et al., 1995; Hofstede, 1980; Joines, Scherer and Scheufele, 2003; Korgankoar and Moschis, 1987; Li et al., 1999; Marcus and Kitayama, 1991; Markus and Oyserman, 1989; May and Greyser, 1989; Mulhern, 1997; Park and Jun, 2003; Peterson, Albaum and Ridgway, 1989; Reynolds, 1974; Reynolds and Darden, 1972; Rohm and Swaminathan, 2004; Sim and Koi, 2002; Steenkamp et al., 1999; Trocchia and Janda, 2000; Vrechopoulos et al, 2001; Wotruba and Pribova, 1995; Yang, 2005. AIMC, 2004; Bordeau et al., 2002; Darian, 1987; Joines, Scherer and Scheufele, 2003; May and Greyser, 1989; Modahl, 2000; Mulhern, 1997; Peterson et al., 1989; Rogers, 2003; Schiffman and Kanuk, 2003; Trocchia and Janda, 2000; Wotruba and Pribova, 1995. AIMC, 2004; Liberal, 1997; Rosenberg and Hirschman, 1980. Bellman et al., 1999; Dahlen, 2002; Dholakia and Usitalo, 2002; Korgankoar and Moschis, 1987; Lohse, Bellman and Johnson, 2000; Modahl, 2000; Sim and Koi, 2002; Steckel, 2000. Eastin, 2002; Hirschman, 1980; Korgankoar and Moschis, 1987; LaRose and Atkin, 1992; Shim and Drake, 1990; Dholakia and Uusitalo, 2002; Rogers, 2003; Yang, 2005.

-

Generational change as a driver of interactive shopping.

It is important to promote Mcommerce using mass communication media to boost the feeling of social belonging. Exposure to Internet and the availability of the appropriate equipment have a positive influence on M-shopping. Being a user of non-store shopping channels such as Internet has a positive influence on Mobile perceived ease-of-use and on Mshopping intention.

Source: Original work. 4.1. Descriptive analysis In relation to the general characteristics of the sample, firstly it should be noted that it comprises Spanish Internet users aged 14 and over. The total sample is composed of 55.4% men and 45.4% women. In terms of analysis by age, a large percentage of the sample belongs to the age segment between 14 and 24 (32%). The middle class predominates (39.3%) and 38.6% live in the capital of the province.

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A significant percentage of those interviewed used Internet very often (45.4%), it should also be noted that 23.2% have acquired some product/service through this direct shopping system (see Table 6). Table 6. Description of the sample Internet user (N = 2,104) Mpurchasers (N = 585)

Non Mpurchasers (N = 1,519)

Male Female

53.7% 46.3%

56.0% 44.0%

55.4% 45.4%

Between 14 and 19 Between 20 and 24 Between 25 and 34 Between 35 and 44 Between 45 and 54 Over 55 High Middle to High Middle Middle to low Low Less than 2,000 2,001-5,000 5,001-10,000 10,001-50,000 50,001-200,000 200,001-500,000 Provincial capital Everyday From 3 to 6 days At least once a week At least once a month Less frequently

38.3% 23.6% 25.3% 9.6% 2.6% 0.7% 14.5% 26.3% 47.9% 10.3% 1.0% 5.8% 7.7% 8.4% 26.0% 14.7% 1.5% 35.9%

8.0% 12.5% 31.6% 22.6% 16.3% 9.0% 21.9% 32.1% 35.9% 9.3% 0.8% 4.9% 6.1% 8.4% 24.6% 13.8% 2.7% 39.6%

16.4% 15.6% 29.8% 19.0% 12.5% 6.7% 19.8% 30.5% 39.3% 9.6% 0.9% 5.2% 6.5% 8.4% 25.0% 14.0% 2.4% 38.6%

40.2% 26.5% 15.7% 13.3% 4.3%

47.5% 23.5% 14.4% 9.8% 4.8%

45.4% 24.3% 14.8% 10.8% 4.7%

Yes No

25.3% 74.7%

17.8% 82.2%

23.2% 76.8%

Characteristic

Gender

Age

Social Class

Population density

Internet use

Experience of Internet shopping

% of total (N = 2,104)

Chi-square

χ2 = 0.800; p = 0.371 χ2 = 421.130 (**); p = 0.000

χ2 = 31.919 (**); p = 0.000

χ2 = 6.891; p = 0.331

χ2 = 9.110; P= 0.232

χ2= 13.559; p = 0.000 (**)

(**) 99% confidence level. Comparison of the M-shopper profile with that of the non M-shopper shows that, generally, sample distribution is fairly uniform in terms of gender and population density as confirmed by the values obtained in the Chi-Square analysis (χ2= 0.800, p = 0.371 for gender and χ2= 6.891, p = 0.331 for area size). The results of this analysis also show that there are no significant differences between M-shopper and non M-shoppers in terms of frequency of Internet use (χ2= 9.110, p = 0.232), as in both cases the frequency of use is very high. The only variables with significant differences are age (χ2= 421.130, p = 0.000), social class (χ2= 31.919, p = 0.000) and on-line shopping experience (χ2= 13.559, p = 0.000). It should be emphasised that the main users of this system are young people, since, generally, the data shows that 61.9% of shoppers are young people between 14 and 24. The least representative group of shoppers is that of interviewees over 45 years old (3.3%). The main reason for this may well be the lack of use and familiarity with the new technologies. In terms of social class, Internet users are mainly from the upper and upper middle classes.

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A significant number of M-shoppers and non M-shoppers use Internet to purchase products/services (23.2%), but the percentage of M-shoppers (25.3%) who use this system for commercial transactions is higher. In terms of product typology (see Table 7), the products most often purchased by Spanish Internet users are Ring tones (22.1%), logos (14.1%) and songs (8.3%). However, they are also beginning to buy some videos (0.9%) and other services such as horoscopes, chats (2.0%). Table 7. Products purchased by Mobile Logos Ringtones Products Songs purchased Videos Others (horoscopes, chats) Not purchased Source: AECE [2004]

(N = 2,104) 14.1% 22.1% 8.3% 0.9% 2.0% 72.2%

In general they are goods of search, intangible and low cost and this result is coherent with the conclusions of the study by Girard, Silverblatt and Korgankoar [2002] who state that search goods will be the most successful in non-store selling channels as they are those for which the perceived purchase risk is lower. It should also be noted that it is likely that the important promotional campaigns on television over recent years for this group of goods has contributed towards generalising acceptance among consumers. The focus group has also confirmed these results as can be seen from participants’ comments: “I buy ringtones and logos on the mobile because they are in fashion, they aren’t expensive and I know exactly what I’m going to get.” “I sometimes use the mobile to participate in TV voting programmes provided it’s not too expensive” 4.2. Logistic regression A logistic regression was used to test the proposed hypotheses. For the regression, Mobile shopper was coded as a dichotomous variable including Internet users who had purchased anything by mobile (n=585) against those who said they had never purchased through this channel (n=1,519). Independent variables included the factors of Gender, Age, Social class; Internet usage patterns and Online buying behaviour (see Table 9). Given that all the model’s independent variables, except for age, are categorical, an adaptation procedure is required before including them in the model. Table 8 shows how the fictitious variables were designed and codified Table 8. Design variables for the logistic regression analysis Original variable Design variables ONLINE EXPOSURE (EXP) EXP1 EXP2 EXP3 EXP4 Every day of the week (EXP1) 1 0 0 0 3 to 6 days a week (EXP2) 0 1 0 0 At least 1 day a week (EXP3) 0 0 1 0 At least once a month (EXP4) 0 0 0 1 ONLINE PURCHASE (PURCHASE) PURCHASE1 Has purchased on Internet 1 Has never purchased on Internet 0 STATUS STATUS1 STATUS2 STATUS3 STATUS4 Upper class (STATUS1) 1 0 0 0 Middle to upper class (STATUS 2) 0 1 0 0 Middle class (STATUS 3) 0 0 1 0 Middle to Lower class (STATUS 4) 0 0 0 1 GENDER GEN1 Man 1 Woman 0

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Table 9. Logistic Regression for predicting membership of Mobile Shopping groups Beta Std. Wald Sig Variable Coef Error AGE -.101 .006 270.678 .000

.903

GENDER

.075

.111

.449

.503

1.077

PURCHASE

.183

.143

1.632

.049

1.201

3.292

.510

EXP

Exp(B)

EXP(1)

-.360

.289

1.554

.213

.697

EXP(2)

-.259

.292

.786

.375

.771

EXP(3)

-.407

.304

1.797

.1480

.665

EXP(4)

-.148

.312

.225

.635

.862

15.380

.004

STATUS STATUS (1)

-.111

.552

.040

.841

.894

STATUS (2)

.098

.544

.032

.857

1.102

STATUS (3)

.384

.540

.508

.476

1.468

STATUS (4)

-.156

.558

.078

.780

.855

INTERCEPT

2.356

.328

51.545

.000

10.553

It can be seen that only the regression coefficients for the variables age, status and experience as Internet shopper are statistically significant (p